library(cowplot)
getwd()
[1] “/home/cirec/Desktop/git_workspace/RMKRDpRCT”
setwd("~/Desktop/git_workspace/RMKRDpRCT/")
# ggdraw() + draw_image(image1, width = 0.5, s)
image_list <- list.files("images/photos/", pattern = ".jpg")
image_list
[1]
“2022-21.GS.C4B.AYT.20.IB-rep1-IITA-TMS-IBA000070_119_Photos_1_2022-07-08-10-03-19.jpg”
[2]
“2022-21.GS.C4B.AYT.20.IB-rep1-IITA-TMS-IBA30572_115_Photos_1_2022-07-08-09-57-17.jpg”
[3]
“2022-21.GS.C4B.AYT.20.IB-rep1-IITA-TMS-IBA980581_102_Photos_1_2022-07-08-09-02-59.jpg”
[4]
“2022-21.GS.C4B.AYT.20.IB-rep1-TMEB419_104_Photos_1_2022-07-08-09-03-49.jpg”
[5]
“2022-21.GS.C4B.AYT.20.IB-rep1-TMS13F1160P0004_116_Photos_1_2022-07-08-09-59-45.jpg”
[6]
“2022-21.GS.C4B.AYT.20.IB-rep1-TMS14F1036P0007_109_Photos_1_2022-07-08-09-28-09.jpg”
[7]
“2022-21.GS.C4B.AYT.20.IB-rep1-TMS18F1083P0032_120_Photos_2_2022-07-08-10-05-08.jpg”
[8]
“2022-21.GS.C4B.AYT.20.IB-rep1-TMS18F1139P0088_107_Photos_1_2022-07-08-09-20-32.jpg”
[9]
“2022-21.GS.C4B.AYT.20.IB-rep1-TMS18F1258P0113_114_Photos_1_2022-07-08-09-55-43.jpg”
[10]
“2022-21.GS.C4B.AYT.20.IB-rep1-TMS18F1258P0114_105_Photos_1_2022-07-08-09-18-24.jpg”
[11]
“2022-21.GS.C4B.AYT.20.IB-rep1-TMS18F1447P0005_117_Photos_1_2022-07-08-10-01-30.jpg”
[12]
“2022-21.GS.C4B.AYT.20.IB-rep1-TMS19F1011P0053_112_Photos_1_2022-07-08-09-52-44.jpg”
[13]
“2022-21.GS.C4B.AYT.20.IB-rep1-TMS19F1189P0046_113_Photos_1_2022-07-08-09-54-17.jpg”
[14]
“2022-21.GS.C4B.AYT.20.IB-rep1-TMS19F1219P0091_111_Photos_1_2022-07-08-09-49-51.jpg”
[15]
“2022-21.GS.C4B.AYT.20.IB-rep2-IITA-TMS-IBA000070_215_Photos_1_2022-07-08-10-41-27.jpg”
[16]
“2022-21.GS.C4B.AYT.20.IB-rep2-IITA-TMS-IBA30572_216_Photos_1_2022-07-08-11-06-38.jpg”
[17]
“2022-21.GS.C4B.AYT.20.IB-rep2-IITA-TMS-IBA980581_210_Photos_1_2022-07-08-10-36-25.jpg”
[18]
“2022-21.GS.C4B.AYT.20.IB-rep2-TMEB419_204_Photos_1_2022-07-08-10-17-39.jpg”
[19]
“2022-21.GS.C4B.AYT.20.IB-rep2-TMS13F1160P0004_201_Photos_1_2022-07-08-10-06-29.jpg”
[20]
“2022-21.GS.C4B.AYT.20.IB-rep2-TMS14F1036P0007_218_Photos_1_2022-07-08-11-11-33.jpg”
[21]
“2022-21.GS.C4B.AYT.20.IB-rep2-TMS18F1310P0145_212_Photos_1_2022-07-08-10-56-55.jpg”
[22]
“2022-21.GS.C4B.AYT.20.IB-rep2-TMS18F1317P0102_206_Photos_1_2022-07-08-10-19-30.jpg”
[23]
“2022-21.GS.C4B.AYT.20.IB-rep2-TMS18F1339P0033_203_Photos_1_2022-07-08-10-32-46.jpg”
[24]
“2022-21.GS.C4B.AYT.20.IB-rep2-TMS18F1447P0005_213_Photos_1_2022-07-08-10-40-07.jpg”
[25]
“2022-21.GS.C4B.AYT.20.IB-rep2-TMS19F1011P0053_202_Photos_1_2022-07-08-10-09-29.jpg”
[26]
“2022-21.GS.C4B.AYT.20.IB-rep2-TMS19F1189P0046_207_Photos_1_2022-07-08-10-34-07.jpg”
[27]
“2022-21.GS.C4B.AYT.20.IB-rep2-TMS19F1219P0091_211_Photos_1_2022-07-08-10-37-05.jpg”
class(image_list[[1]])
[1] “character”
# for(i in 1:length(image_list)){
# img <- paste0("images/photos/",image_list[[2]])
# p1 <- ggdraw() + draw_image(img, scale = 0.9)
# class(p1)
# ggplot2::ggsave(plot = p1, path = "images/plots/",filename = image[[2]])
# p2 <- ggdraw() + draw_image(img, scale = 0.9)
# p2
# plot_grid(p1,p2)
# }
library(stringr)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ forcats 0.5.1
## ✔ readr 2.1.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
trial <- read.csv("tables/AYT20_SINDEX.csv")
# View(trial)
###attach(trial)
trial <- trial %>% select(-c("SN","X","Selected", "X.1"))
get_genotypes <- function(){
return(trial$accession_name)
}
genotype <- as.list(get_genotypes())
image_list
[1]
“2022-21.GS.C4B.AYT.20.IB-rep1-IITA-TMS-IBA000070_119_Photos_1_2022-07-08-10-03-19.jpg”
[2]
“2022-21.GS.C4B.AYT.20.IB-rep1-IITA-TMS-IBA30572_115_Photos_1_2022-07-08-09-57-17.jpg”
[3]
“2022-21.GS.C4B.AYT.20.IB-rep1-IITA-TMS-IBA980581_102_Photos_1_2022-07-08-09-02-59.jpg”
[4]
“2022-21.GS.C4B.AYT.20.IB-rep1-TMEB419_104_Photos_1_2022-07-08-09-03-49.jpg”
[5]
“2022-21.GS.C4B.AYT.20.IB-rep1-TMS13F1160P0004_116_Photos_1_2022-07-08-09-59-45.jpg”
[6]
“2022-21.GS.C4B.AYT.20.IB-rep1-TMS14F1036P0007_109_Photos_1_2022-07-08-09-28-09.jpg”
[7]
“2022-21.GS.C4B.AYT.20.IB-rep1-TMS18F1083P0032_120_Photos_2_2022-07-08-10-05-08.jpg”
[8]
“2022-21.GS.C4B.AYT.20.IB-rep1-TMS18F1139P0088_107_Photos_1_2022-07-08-09-20-32.jpg”
[9]
“2022-21.GS.C4B.AYT.20.IB-rep1-TMS18F1258P0113_114_Photos_1_2022-07-08-09-55-43.jpg”
[10]
“2022-21.GS.C4B.AYT.20.IB-rep1-TMS18F1258P0114_105_Photos_1_2022-07-08-09-18-24.jpg”
[11]
“2022-21.GS.C4B.AYT.20.IB-rep1-TMS18F1447P0005_117_Photos_1_2022-07-08-10-01-30.jpg”
[12]
“2022-21.GS.C4B.AYT.20.IB-rep1-TMS19F1011P0053_112_Photos_1_2022-07-08-09-52-44.jpg”
[13]
“2022-21.GS.C4B.AYT.20.IB-rep1-TMS19F1189P0046_113_Photos_1_2022-07-08-09-54-17.jpg”
[14]
“2022-21.GS.C4B.AYT.20.IB-rep1-TMS19F1219P0091_111_Photos_1_2022-07-08-09-49-51.jpg”
[15]
“2022-21.GS.C4B.AYT.20.IB-rep2-IITA-TMS-IBA000070_215_Photos_1_2022-07-08-10-41-27.jpg”
[16]
“2022-21.GS.C4B.AYT.20.IB-rep2-IITA-TMS-IBA30572_216_Photos_1_2022-07-08-11-06-38.jpg”
[17]
“2022-21.GS.C4B.AYT.20.IB-rep2-IITA-TMS-IBA980581_210_Photos_1_2022-07-08-10-36-25.jpg”
[18]
“2022-21.GS.C4B.AYT.20.IB-rep2-TMEB419_204_Photos_1_2022-07-08-10-17-39.jpg”
[19]
“2022-21.GS.C4B.AYT.20.IB-rep2-TMS13F1160P0004_201_Photos_1_2022-07-08-10-06-29.jpg”
[20]
“2022-21.GS.C4B.AYT.20.IB-rep2-TMS14F1036P0007_218_Photos_1_2022-07-08-11-11-33.jpg”
[21]
“2022-21.GS.C4B.AYT.20.IB-rep2-TMS18F1310P0145_212_Photos_1_2022-07-08-10-56-55.jpg”
[22]
“2022-21.GS.C4B.AYT.20.IB-rep2-TMS18F1317P0102_206_Photos_1_2022-07-08-10-19-30.jpg”
[23]
“2022-21.GS.C4B.AYT.20.IB-rep2-TMS18F1339P0033_203_Photos_1_2022-07-08-10-32-46.jpg”
[24]
“2022-21.GS.C4B.AYT.20.IB-rep2-TMS18F1447P0005_213_Photos_1_2022-07-08-10-40-07.jpg”
[25]
“2022-21.GS.C4B.AYT.20.IB-rep2-TMS19F1011P0053_202_Photos_1_2022-07-08-10-09-29.jpg”
[26]
“2022-21.GS.C4B.AYT.20.IB-rep2-TMS19F1189P0046_207_Photos_1_2022-07-08-10-34-07.jpg”
[27]
“2022-21.GS.C4B.AYT.20.IB-rep2-TMS19F1219P0091_211_Photos_1_2022-07-08-10-37-05.jpg”
get_images <- function(genotype, pattern){
list_gen <- c()
for(i in 1:length(image_list)){
if(stringr::str_detect(image_list[[i]], regex(genotype)) == TRUE){
if(stringr::str_detect(image_list[[i]], regex(pattern)) == TRUE){
list_gen[[length(list_gen) + 1]] <- image_list[[i]]
}
}
}
return(list_gen)
}
table_chunk <- function(gene){
df <- trial %>% filter(accession_name == gene)
FYLD <- df$FYLD
DYLD <- df$DYLD
DM <- df$DM
SI <- df$SINDEX
new_df <- data.frame(Trait = c("FYLD", "DYLD", "DM", "SINDEX"),
Value = c(FYLD, DYLD, DM, SI))
return(new_df)
}
library(knitr)
class(genotype)
[1] “list”
lapply(1:length(genotype), function(i){
rep1 <- get_images(genotype = genotype[[i]], "rep1")
rep2 <- get_images(genotype = genotype[[i]], "rep2")
if(!is.null(rep1) || !is.null(rep2)){
reps <- append(rep1, rep2)
img <- c()
for(j in 1:length(reps)){
path_ <- paste0("images/photos/",reps[[j]])
img[[length(img) + 1]] <- path_
}
length(img)
print(genotype[[i]])
print(kable(table_chunk(genotype[[i]])))
p1 <- ggdraw() + draw_image(img[[1]], scale = 0.9)
if(length(img) > 1){
p2 <- ggdraw() + draw_image(img[[2]], scale = 0.9)
px <- plot_grid(p1,p2)
x <- ggsave("per.jpg", px)
cat("")
} else if(length(img) == 1){
p3 <- plot_grid(p1)
py <- ggsave("jer.jpg", p3)
cat("")
}
}
})
[1] “IITA-TMS-IBA000070”
| Trait | Value |
|---|---|
| FYLD | 37.87 |
| DYLD | 14.99 |
| DM | 40.89 |
| SINDEX | 106.80 |
## Saving 7 x 5 in image
[1] “TMS18F1083P0032”
| Trait | Value |
|---|---|
| FYLD | 36.60 |
| DYLD | 14.88 |
| DM | 42.08 |
| SINDEX | 98.45 |
## Saving 7 x 5 in image
[1] “TMS19F1219P0091”
| Trait | Value |
|---|---|
| FYLD | 38.33 |
| DYLD | 14.44 |
| DM | 37.81 |
| SINDEX | 91.42 |
## Saving 7 x 5 in image
[1] “TMS18F1139P0088”
| Trait | Value |
|---|---|
| FYLD | 38.15 |
| DYLD | 14.77 |
| DM | 39.59 |
| SINDEX | 88.29 |
## Saving 7 x 5 in image
[1] “TMS18F1317P0102”
| Trait | Value |
|---|---|
| FYLD | 36.48 |
| DYLD | 14.61 |
| DM | 40.84 |
| SINDEX | 57.63 |
## Saving 7 x 5 in image
[1] “TMS19F1189P0046”
| Trait | Value |
|---|---|
| FYLD | 37.78 |
| DYLD | 14.24 |
| DM | 37.39 |
| SINDEX | 55.38 |
## Saving 7 x 5 in image
[1] “TMS18F1258P0113”
| Trait | Value |
|---|---|
| FYLD | 36.74 |
| DYLD | 14.44 |
| DM | 39.71 |
| SINDEX | 35.78 |
## Saving 7 x 5 in image
[1] “TMS18F1447P0005”
| Trait | Value |
|---|---|
| FYLD | 36.21 |
| DYLD | 14.46 |
| DM | 40.38 |
| SINDEX | 28.63 |
## Saving 7 x 5 in image
[1] “TMS18F1339P0033”
| Trait | Value |
|---|---|
| FYLD | 36.53 |
| DYLD | 14.34 |
| DM | 39.42 |
| SINDEX | 26.03 |
## Saving 7 x 5 in image
[1] “TMS19F1011P0053”
| Trait | Value |
|---|---|
| FYLD | 34.62 |
| DYLD | 14.38 |
| DM | 42.22 |
| SINDEX | 15.39 |
## Saving 7 x 5 in image
[1] “TMEB419”
| Trait | Value |
|---|---|
| FYLD | 35.27 |
| DYLD | 14.26 |
| DM | 40.58 |
| SINDEX | -3.40 |
## Saving 7 x 5 in image
[1] “TMS18F1258P0114”
| Trait | Value |
|---|---|
| FYLD | 35.30 |
| DYLD | 14.02 |
| DM | 39.34 |
| SINDEX | -20.63 |
## Saving 7 x 5 in image
[1] “TMS13F1160P0004”
| Trait | Value |
|---|---|
| FYLD | 34.29 |
| DYLD | 14.05 |
| DM | 40.86 |
| SINDEX | -32.66 |
## Saving 7 x 5 in image
[1] “TMS14F1036P0007”
| Trait | Value |
|---|---|
| FYLD | 34.72 |
| DYLD | 14.00 |
| DM | 39.62 |
| SINDEX | -44.70 |
## Saving 7 x 5 in image
[1] “IITA-TMS-IBA30572”
| Trait | Value |
|---|---|
| FYLD | 33.36 |
| DYLD | 13.81 |
| DM | 40.61 |
| SINDEX | -55.43 |
## Saving 7 x 5 in image
[1] “TMS18F1310P0145”
| Trait | Value |
|---|---|
| FYLD | 32.94 |
| DYLD | 13.73 |
| DM | 41.00 |
| SINDEX | -55.87 |
## Saving 7 x 5 in image
[1] “IITA-TMS-IBA980581”
| Trait | Value |
|---|---|
| FYLD | 33.74 |
| DYLD | 13.56 |
| DM | 38.80 |
| SINDEX | -93.27 |
## Saving 7 x 5 in image
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